Burn Patient Outcome Prediction Across Centers With Machine Learning
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Solution Overview
Problem
Burn care centers experience variability in patient mortality and length of stay (LOS) outcomes, with existing estimates being patient-specific and center-dependent, lacking a reliable, risk-adjusted statistical model for comparison and quality improvement.
Innovation Solution
A system and method using machine learning techniques, particularly gradient boosted regression models like CatBoost, to predict patient outcomes such as mortality and LOS across multiple healthcare centers, leveraging anonymized patient data for training models that provide patient-specific insights and center performance comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional estimation methods (1 day per % TBSA) are used to predict length of stay, then the prediction process is simple and quick, but the accuracy and reliability of the prediction is poor and center-dependent
Solution Approach 1:
The patent transforms the simple linear estimation (1 day per % TBSA) into a complex machine learning model that incorporates multiple parameters including patient demographics, burn characteristics, comorbidities, and treatment factors. This parameter expansion enables accurate, risk-adjusted predictions while resolving the center-dependency issue through standardized multi-center training data.
Solution Approach 2:
The patent replaces the mechanical calculation method (simple multiplication formula) with an intelligent system using gradient boosted regression models and other machine learning algorithms. This substitution enables the system to automatically learn complex non-linear relationships from data, achieving superior prediction accuracy without manual adjustment of estimation factors.
2Reliability
If machine learning models are trained using multi-center patient data, then the prediction reliability and generalizability improve, but the data processing and model training complexity increases
Solution Approach 1:
The patent segments the complex multi-center data processing task into distinct modular components: data collection from multiple sources, data cleaning and standardization, feature engineering, model training, validation, and deployment. This segmentation manages complexity while enabling reliable predictions through systematic processing of multi-center data.
Solution Approach 2:
The patent creates a universal predictive system that can handle diverse data sources and patient populations across multiple centers. The gradient boosted regression model and other machine learning algorithms are designed to be center-agnostic, learning general patterns from aggregated data that apply universally across different healthcare settings, thereby improving reliability while managing complexity through standardization.
3Loss of information
If patient-specific insights and center performance comparisons are provided, then the quality of care assessment improves, but the computational resources and time required increase
Solution Approach 1:
The patent performs preliminary actions by pre-training the machine learning models on extensive multi-center historical data before deployment. This pre-training establishes baseline predictions and performance metrics that can be quickly generated for new patients without requiring extensive real-time computation, thus providing comprehensive information while minimizing processing time during actual clinical use.
Solution Approach 2:
The patent uses copying by generating standardized prediction outputs and performance metrics that can be rapidly replicated across different centers and patients. Once the model is trained on comprehensive data, it produces consistent, information-complete predictions through efficient computation, avoiding the need to reprocess all training data for each new prediction request.
Data Source
AI summary
The disclosed technology includes a method for determining outcomes of patients across healthcare centers, the method including: receiving, at a computer system, patient data for patients in healthcare centers, training, using machine learning techniques and a portion of the data for the burn patients, a predictive model to predict patient outcomes based on assessing patient data for patients across the healthcare centers, and returning the trained predictive model for runtime use. During runtime use, the method can include: providing the patient data as input to the predictive model, receiving, as output, predicted patient outcomes for at least one patient amongst the patients in the healthcare centers, generating, based on the predicted patient outcomes, at least one care recommendation, generating output representative of the predicted patient outcomes and the care recommendation, and transmitting the output to a user computing device for presentation in a graphical user interface (GUI) display.